Multi-View Learning. Learning on data with multiple views has proven effective in a variety of tasks. CCA-based multi-view models [1]}, [2]}, [3]}, [4]}, [5]}, [6]} are representative ones that have been widely used in multi-view representation learning. These models essentially seek a common representation by maximizing the correlation between different views. Considering common and exclusive information, hierarchical multi-modal metric learning (HM3L) [7]} explicitly learns shared multi-view and view-specific metrics, while \(AE^2\) -Nets [8]} implicitly learn a complete (view-specific and shared multi-view) representation for classification. Recently, the methods [9]}, [10]}, [11]}, [12]} based on contrastive learning have also achieved good performance. Due to its effectiveness, multi-view learning has been widely used in various applications [13]}, [14]}, [15]}, [16]}.
